# Calibration of LOFAR data on the cloud

## TL;DR

This paper demonstrates that cloud infrastructures can effectively calibrate LOFAR data, offering advantages in software management, flexibility, and resource on-demand, with cost-effectiveness depending on dataset volume.

## Contribution

It provides a comprehensive evaluation of cloud-based calibration pipelines for LOFAR data, highlighting their feasibility and benefits over traditional infrastructures.

## Key findings

- Cloud calibration pipelines are feasible and efficient.
- Ease of software installation and maintenance in the cloud.
- Cost-effectiveness depends on dataset volume.

## Abstract

New scientific instruments are starting to generate an unprecedented amount of data. LOFAR, one of the Square Kilometre Array pathfinders, is already producing data on a petabyte scale. The calibration of these data presents a huge challenge for final users: a) extensive storage and computing resources are required; b) the installation and maintenance of the processing software is not trivial; and c) the requirements of (experimental) calibration pipelines are quickly evolving. After encountering some limitations in classical infrastructures, we investigated the viability of cloud infrastructures as a solution. We found that the installation and operation of LOFAR data calibration pipelines is not only possible, but can also be efficient in cloud infrastructures. The main advantages were: (1) ease of software installation and maintenance, and the availability of standard APIs and tools, widely used in the industry; this reduces the requirement for significant manual intervention, which can have a highly negative impact; (2) flexibility to adapt the infrastructure to the needs of the problem, especially as those demands change over time; (3) on-demand consumption of (shared) resources. We found no significant impediments associated with the speed of data transfer, the use of external block storage, or the memory available. However, the availability of scratch storage areas of an appropriate size is critical. Finally, we considered the cost-effectiveness of a commercial cloud like Amazon Web Services. While it is more expensive than the operation of a large, fully-utilised cluster completely dedicated to LOFAR data reduction, its costs are competitive if the number of datasets to be analysed is not high, or if the costs of maintaining the dedicated system become high. Coupled with the advantages discussed above, this suggests that a cloud infrastructure may be favourable for many users.